Mathias Lechner
Chief Technology Officer at Liquid AI
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolMathias Lechner is a CTO and co-founder of Liquid AI with a decade of experience translating cutting-edge research into production-grade machine learning systems. He combines academic depth—PhD in Computer Science and roles as MIT research affiliate and former postdoc—with hands-on engineering, evidenced by his open-source implementations of neural circuit policies (NCP/LTC/CfC) in PyTorch and TensorFlow. Based in San Francisco, he leads product and platform decisions while remaining active in research on irregularly sampled time-series and recurrent state inference. His background across IST Austria and TU Wien gives him a strong foundation in both theory and systems engineering, and his work often bridges prototype research code and deployable ML infrastructure.
10 years of coding experience
2 years of employment as a software developer
Vienna University of TechnologyDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Institute of Science and Technology Austria
English, German
Github Skills (8)
pytorch10
machine-learning10
recurrent-neural-networks10
deep-learning10
tensorflow10
data-analysis9
time-series9
keras9
Programming languages (3)
Github contributions (5)
mlech26l/ncps
Aug 2020 - Jan 2023
PyTorch and TensorFlow implementation of NCP, LTC, and CfC wired neural models
Role in this project:
ML Engineer
ML EngineerContributions:2 releases, 1 review, 87 commits in 2 years 5 months
Contributions summary:Mathias implemented and refined various aspects of a PyTorch and TensorFlow-based model for neural circuit policies. Their contributions included example implementations and fixes for irregularly sampled time-series data handling within the LTC cell, as well as the addition of experimental PyTorch bindings. The user also developed a tutorial example demonstrating the inference of hidden states within the recurrent neural network.
pytorchtensorflowncprecurrent-neural-networknature-machine-intelligence
pyhopper/pyhopper
Mar 2021 - Jan 2023
PyHopper is a hyperparameter optimizer, made specifically for high-dimensional problems arising in machine learning research.
Contributions:123 commits, 2 PRs, 102 pushes in 1 year 10 months
hyperparametersmachine-learningdeep-learningpythonoptimization
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